JH – 20100528 #4 (9?) RetroV:
\nLooks at vids put out by the campaigns during the 2008 election. Says they were not viral, but that views were driven by channel, and in fact, equilibrium reached faster at the end of the election becuase more videos were put out faster, pushing older vids down the list (reducing the likelyhood that they will get views.
\nTalks about 3 kinds / defs of viral.
\n1) raw, num views def
\n2) process, epidemiological, network diffusion, word-o-mouth. Mentions vids passed by email, embeds, links, and blogged about. Faberge Shampoo commercial.
\n3) functional, sigmoid curve like distribution of cumulative views. His discusion about this is based on a web page that has about 300 words of text on it (link at bottom). Clearly, Boyten didn't look to much into virality.
\nNotes blog visitation follows power law distribution where you have a few eliets, a good number of middle of the raod, and a massive amount of sites/blogs in the tail. Similar to income distrutions. Possibly like Pareto distribution.
\nAlso says campaign vids not viral and hit half of views faster than non-campaign vids
\nUses regression in completely inappropriate way. lame. Political scientists and statistics just may not mix. Though, he does come up with a multiplier (the b1 coeffificent) for Y(t-1) to plot Y(t). In my brief read into epidemic lit (Boyten's refs) his regression coefficent is the infection rate. Why doesn't he say that?
\nY(t)=b0 + b1Y(t-1). Effectivly this is smiliar to a equilibrium diffeQ type equation where there is some asymtotic limit that is approached. Speed of growth decays to zero over time.
\n\n
Boynton: “It is hard to imagine that 17 blog posts can generate very many views, and certainly they are not going to generate going viral.”
\nBoynton: “If not blog posts what is generating the views? The answer seems very simple. There is a directly relevant “database” - the channels, and to a lesser degree, the websites of the campaigns.”
\n\n
\n
http://www.apsnet.org/education/advancedplantpath/topics/Epidemiology/UpperLimit.htm
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Author(s) agree to transfer their copyrights in their article/paper to the Association for the Advancement of Artificial Intelligence (AAAI), in order to deal with future requests for reprints, translations, anthologies, reproductions, excerpts, and other publications. This grant will include, without limitation, the entire copyright in the article/paper in all countries of the world, including all renewals, extensions, and reversions thereof, whether such rights current exist or hereafter come into effect, and also the exclusive right to create electronic versions of the article/paper, to the extent that such right is not subsumed under copyright. 2. The author(s) warrants that they are the sole author and owner of the copyright in the above article/paper, except for those portions shown to be in quotations; that the article/paper is original throughout; and that the undersigned right to make the grants set forth above is complete and unencumbered. 3. The author(s) agree that if anyone brings any claim or action alleging facts that, if true, constitute a breach of any of the foregoing warranties, the author(s) will hold harmless and indemnify AAAI, their grantees, their licensees, and their distributors against any liability, whether under judgment, decree, or compromise, and any legal fees and expenses arising out of that claim or actions, and the undersigned will cooperate fully in any defense AAAI may make to such claim or action. Moreover, the undersigned agrees to cooperate in any claim or other action seeking to protect or enforce any right the undersigned has granted to AAAI in the article/paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, the undersigned agrees to reimburse whomever brings such claim or action for expenses and attorneys’ fees incurred therein. 4. Author(s) retain all proprietary rights other than copyright (such as patent rights). 5. Author(s) may make personal reuse of all or portions of the above article/paper in other works of their own authorship. 6. Author(s) may reproduce, or have reproduced, their article/paper for the author’s personal use, or for company use provided that AAAI copyright and the source are indicated, and that the copies are not used in a way that implies AAAI endorsement of a product or service of an employer, and that the copies per se are not offered for sale. The foregoing right shall not permit the posting of the article/paper in electronic or digital form on any computer network, except by the author or the author’s employer, and then only on the author’s or the employer’s own web page or ftp site. Such web page or ftp site, in addition to the aforementioned requirements of this Paragraph, must provide an electronic reference or link back to the AAAI electronic server, and shall not post other AAAI copyrighted materials not of the author’s or the employer’s creation (including tables of contents with links to other papers) without AAAI’s written permission. 7. Author(s) may make limited distribution of all or portions of their article/paper prior to publication. 8. In the case of work performed under U.S. Government contract, AAAI grants the U.S. Government royalty-free permission to reproduce all or portions of the above article/paper, and to authorize others to do so, for U.S. Government purposes. 9. In the event the above article/paper is not accepted and published by AAAI, or is withdrawn by the author(s) before acceptance by AAAI, this agreement becomes null and void.", "extra": "Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. 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